Edge-Aware Spatial Noise Filter for Video Frame Detail Preservation
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Solution Overview
Problem
Conventional spatial noise filtering in video processing often blurs edges and fine details, which is undesirable for high-quality, high-definition images, as it reduces image quality and may not effectively remove noise without compromising edge preservation.
Innovation Solution
Edge-aware spatial noise filtering techniques that account for contrast changes at edges, allowing for adaptive filtering that preserves or enhances edges while removing random noise, using weighting factors to determine the contribution of adjacent pixels in the filtering process, thereby reducing or eliminating blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional spatial noise filtering is applied to remove random noise from video frames, then noise filtering effectiveness is improved, but edge blurring and loss of fine details occur
Solution Approach 1:
The patent applies different filtering strengths to different regions of the image based on local edge detection. Edge regions are identified and given reduced filtering intensity to preserve sharpness, while non-edge regions receive full filtering intensity to remove noise effectively. This local differentiation resolves the contradiction by making the filtering quality spatially variable rather than uniform.
Solution Approach 2:
The filtering process dynamically adjusts its parameters based on local image characteristics. The filter strength is modulated according to edge strength and orientation detected in each local region, allowing the system to adapt between noise removal and edge preservation modes depending on the local content. This dynamic adaptation enables the filter to maintain both noise filtering effectiveness and edge sharpness.
2Reliability
If strong spatial filtering is applied to achieve high noise removal, then noise filtering effectiveness is improved, but image quality deteriorates due to blurring
Solution Approach 1:
The patent implements spatially varying filter strength where edge regions receive attenuated filtering compared to non-edge regions. This local quality differentiation ensures that noise removal is applied strongly where it benefits image quality while preserving critical structural information at edges, thus preventing information loss while maintaining noise filtering effectiveness.
Solution Approach 2:
The patent introduces edge detection and weighting mechanisms as intermediary processes between the input image and the filtering operation. These intermediaries analyze local image characteristics and modulate the filtering strength accordingly, acting as a mediator that balances noise removal with detail preservation by adjusting filter parameters based on local edge properties.
Data Source
AI summary
A technique to perform edge-aware spatial noise filtering that may filter random noise from frames while maintaining the edges in the frames. The technique may include receiving a frame comprising a pint ht of pixels, filtering a subset of the plurality of pixels based on a weighting factor associated with each pixel of the subset of pixels, wherein the weighting factor is at least in part based on a difference in pixel value between the pixel and the individual pixels in the subset, and providing the filtered pixel to an encoder for encoding. Example implementation may include a spatial noise filter to receive an image, the noise level, and configuration parameters, and configured to determine weighting factors of pixels of the image based on differences in pixel values and a set of configuration parameters, and further configured to filter noise from the image based on the weighting factors of the pixels.